This library provides functionality for rapidly sharing and retrieving word embeddings over the internet. Additional information on the VecShare framework can be found at: https://bit.ly/VecShare **(Accepted at EMNLP 2017)**.
Download at `pip install vecshare`
Supported Functions
The VecShare Python library currently supports:
* [`check`](check-available-embeddings): See available embeddings
* [`format`](embedding-upload-or-update): Autoformat a header to upload an embedding to the data store or Compress an embedding
* [`update`](embedding-upload-or-update): Update an existing embedding or its metadata
* [`query`](embedding-query): Look up word vectors from a specific embedding
* [`extract`:](embedding-extraction) Download word vectors for only the vocabulary of a specific corpus
* [`download`](full-embedding-download): Download an entire shared embedding
Supported Selection Methods
* `maxtkn`: Select embedding trained on most tokens
* `simscore`: Select embedding scoring highest on 9 set similarity task
* `avgrank`: Select embedding with highest avg rank signature score (See https://bit.ly/VecShare)